Lecture 15: Information Retrieval

Following the rectangular tables of relational databases and the triangular trees of semistructured data, the remaining Inf1-DA lectures will address the representation and analysis of more unstructured data. Today’s lecture provided a brief introduction to the classic information retrieval task of searching a large collection of documents to find those that match a simple query.
The focus here is not on specific algorithms or data representations, but on specifying the problem, how to recognise when you have a solution, and how to rate the performance of different competing solutions. In this case that means distinguishing between precision and recall in information retrieval; considering how each might be important in different problem domains; and the use of blends like the F-score to combine both measures.

The lecture finished with material on IBM’s Watson system using all kinds of data and analysis, including information retrieval, to perform question-answering on Jeopardy!.

University server disconnection today means that Panopto did not record the lecture; and there are also no recordings of this specific topic from previous years. My apologies. Instead I can offer the following: